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Record W4220894541 · doi:10.1002/dvdy.465

Normal development in <i>Xenopus laevis</i>: A complementary staging table for the skull based on cartilage and bone

2022· article· en· W4220894541 on OpenAlexafffund
Erin M. MacKenzie, Jade B. Atkins, Dana Korneisel, Alanna S. Cantelon, Iain W. McKinnell, Hillary C. Maddin

Bibliographic record

VenueDevelopmental Dynamics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDevelopmental Biology and Gene Regulation
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSkullBiologyOssificationAnatomyXenopusCartilageGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Xenopus laevis is a widely used model organism in the fields of genetics and development, and more recently evolution. At present, the most widely used staging table for X. laevis is based primarily on external features and does not describe the corresponding skull development in detail. Here, we describe skull development in X. laevis, complete with labeled figures, for each relevant stage in the most widely used staging table. RESULTS: We find skull development in X. laevis is, for the most part, distinct at each of the previously established stages based on external anatomy. However, variation does exist in the timing of onset of ossification of certain bones in the skull, which results in a range of stages where a skull element first ossifies. The overall sequence of ossification is less variable than the timing of ossification onset. CONCLUSIONS: While events in skull development vary somewhat between specimens, and in comparison, to external events, this staging table is useful in showing both when bones first appear and for documenting the range of temporal variance in X. laevis skull development more accurately than previously done. Furthermore, when only skull data are available, the approximate stage of a specimen can now be determined.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.214
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes2
Has abstractyes

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